





"""simple docstring"""


import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_ta import TaTokenizer
else:
		__magic_name__       :					Union[str, Any]						=			None


__magic_name__       :					Union[str, Any]						=			logging.get_logger(__name__)

__magic_name__       :					Optional[int]						=			{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__magic_name__       :					Optional[Any]						=			{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__magic_name__       :					int						=			{
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}






class 							lowercase__			(   __snake_case	):

			"""simple docstring"""
			__lowerCAmelCase     :				Dict										=  VOCAB_FILES_NAMES
			__lowerCAmelCase     :				List[str]										=  PRETRAINED_VOCAB_FILES_MAP
			__lowerCAmelCase     :				List[Any]										=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCAmelCase     :				str										=  ["""input_ids""", """attention_mask"""]
			__lowerCAmelCase     :				Dict										=  TaTokenizer

			__lowerCAmelCase     :				List[int]										=  []







			def __init__(      self						,   _A=None						,   _A=None						,   _A="</s>"						,   _A="<unk>"						,   _A="<pad>"						,   _A=1_0_0						,   _A=None						,   **_A						,   ):






									'''simple docstring'''







									if extra_ids > 0 and additional_special_tokens is None:
															UpperCamelCase				:		Optional[int]				   =			[f"""<extra_id_{i}>""" for i in range(__SCREAMING_SNAKE_CASE    )]
									elif extra_ids > 0 and additional_special_tokens is not None:
															# Check that we have the right number of extra special tokens
															UpperCamelCase				:		Tuple				   =			len(set(filter(lambda _A    : bool("""extra_id_""" in str(__SCREAMING_SNAKE_CASE    )    )						,   __SCREAMING_SNAKE_CASE    )    )    )
															if extra_tokens != extra_ids:
																					raise ValueError(
																					    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																					    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
																					    """ tokens"""    )

									super().__init__(
									    __SCREAMING_SNAKE_CASE						,   tokenizer_file=__SCREAMING_SNAKE_CASE						,   eos_token=__SCREAMING_SNAKE_CASE						,   unk_token=__SCREAMING_SNAKE_CASE						,   pad_token=__SCREAMING_SNAKE_CASE						,   extra_ids=__SCREAMING_SNAKE_CASE						,   additional_special_tokens=__SCREAMING_SNAKE_CASE						,   **__SCREAMING_SNAKE_CASE						,   )

									UpperCamelCase				:		Tuple				   =			vocab_file
									UpperCamelCase				:		Tuple				   =			False if not self.vocab_file else True
									UpperCamelCase				:		Optional[int]				   =			extra_ids







			@staticmethod
			def   _a      (      _A						,   _A						,   _A    ):






									'''simple docstring'''







									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
															UpperCamelCase				:		Optional[int]				   =			TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
															if init_max_model_length is not None and init_max_model_length != max_model_length:
																					return init_max_model_length
															elif init_max_model_length is None:
																					warnings.warn(
																					    """This tokenizer was incorrectly instantiated with a model max length of"""
																					    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																					    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
																					    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
																					    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
																					    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																					    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																					    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
																					    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""						,   __SCREAMING_SNAKE_CASE						,   )

									return max_model_length







			def   _a      (      self						,   _A						,   _A = None    ):






									'''simple docstring'''







									if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""    )

									if not os.path.isdir(__SCREAMING_SNAKE_CASE    ):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
															return
									UpperCamelCase				:		Optional[int]				   =			os.path.join(
									    __SCREAMING_SNAKE_CASE						,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

									if os.path.abspath(self.vocab_file    ) != os.path.abspath(__SCREAMING_SNAKE_CASE    ):
															copyfile(self.vocab_file						,   __SCREAMING_SNAKE_CASE    )
															logger.info(f"""Copy vocab file to {out_vocab_file}"""    )

									return (out_vocab_file,)







			def   _a      (      self						,   _A						,   _A = None    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
															return self.prefix_tokens + token_ids_a
									else:
															UpperCamelCase				:		Optional[Any]				   =			token_ids_a + [self.eos_token_id]
															return self.prefix_tokens + token_ids_a + token_ids_a







			def   _a      (      self						,   _A						,   _A = None    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			[self.eos_token_id]

									if token_ids_a is None:
															return len(token_ids_a + eos    ) * [0]
									return len(token_ids_a + eos + token_ids_a + eos    ) * [0]







			def   _a      (      self    ):






									'''simple docstring'''







									return list(
									    set(filter(lambda _A    : bool(re.search(r"""<extra_id_\d+>"""						,   __SCREAMING_SNAKE_CASE    )    ) is not None						,   self.additional_special_tokens    )    )    )







			def   _a      (      self    ):






									'''simple docstring'''







									return [self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE    ) for token in self.get_sentinel_tokens()]











"""simple docstring"""





import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops

  from transformers import GradientAccumulator, create_optimizer






@require_tf
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Dict   )	->						str:
      self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		len(__SCREAMING_SNAKE_CASE   )   )
      for a, b in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        self.assertAlmostEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		delta=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						int:
      lowerCamelCase_       	=		GradientAccumulator()
      accumulator([tf.constant([1.0, 2.0]   )]   )
      accumulator([tf.constant([-2.0, 1.0]   )]   )
      accumulator([tf.constant([-1.0, 2.0]   )]   )
      with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
        accumulator([tf.constant([1.0, 1.0]   ), tf.constant([2.0, 2.0]   )]   )
      self.assertEqual(accumulator.step			,		3   )
      self.assertEqual(len(accumulator.gradients   )			,		1   )
      self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()			,		[-2.0, 5.0]			,		tol=1e-2   )
      accumulator.reset()
      self.assertEqual(accumulator.step			,		0   )
      self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()			,		[0.0, 0.0]			,		tol=1e-2   )






    def   UpperCamelCase			(			self	:				str   )	->						Any:
      lowerCamelCase_       	=		None
      ops.enable_eager_execution_internal()
      lowerCamelCase_       	=		tf.config.list_physical_devices('CPU'   )
      if len(__SCREAMING_SNAKE_CASE   ) == 1:
        tf.config.set_logical_device_configuration(
            physical_devices[0]			,		[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]   )
      lowerCamelCase_       	=		tf.config.list_logical_devices(device_type='CPU'   )
      lowerCamelCase_       	=		tf.distribute.MirroredStrategy(devices=devices[:2]   )

      with strategy.scope():
        lowerCamelCase_       	=		GradientAccumulator()
        lowerCamelCase_       	=		tf.Variable([4.0, 3.0]   )
        lowerCamelCase_	,      lowerCamelCase_       	=		create_optimizer(5e-5			,		10			,		5   )
        lowerCamelCase_       	=		tf.Variable([0.0, 0.0]			,		trainable=__SCREAMING_SNAKE_CASE   )

      def accumulate_on_replica(__SCREAMING_SNAKE_CASE	:				Union[str, Any]   ):
        accumulator([gradient]   )

      def apply_on_replica():
        optimizer.apply_gradients(list(zip(accumulator.gradients			,		[variable]   )   )   )

      @tf.function
      def accumulate(__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Any   ):
        with strategy.scope():
          lowerCamelCase_       	=		strategy.experimental_local_results(__SCREAMING_SNAKE_CASE   )
          local_variables[0].assign(__SCREAMING_SNAKE_CASE   )
          local_variables[1].assign(__SCREAMING_SNAKE_CASE   )
          strategy.run(__SCREAMING_SNAKE_CASE			,		args=(gradient_placeholder,)   )

      @tf.function
      def apply_grad():
        with strategy.scope():
          strategy.run(__SCREAMING_SNAKE_CASE   )

      def _check_local_values(__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				List[str]   ):
        lowerCamelCase_       	=		strategy.experimental_local_results(accumulator._gradients[0]   )
        self.assertListAlmostEqual(values[0].value()			,		__SCREAMING_SNAKE_CASE			,		tol=1e-2   )
        self.assertListAlmostEqual(values[1].value()			,		__SCREAMING_SNAKE_CASE			,		tol=1e-2   )

      accumulate([1.0, 2.0]			,		[-1.0, 1.0]   )
      accumulate([3.0, -1.0]			,		[-1.0, -1.0]   )
      accumulate([-2.0, 2.0]			,		[3.0, -2.0]   )
      self.assertEqual(accumulator.step			,		3   )
      _check_local_values([2.0, 3.0]			,		[1.0, -2.0]   )
      apply_grad()
      self.assertListAlmostEqual(variable.value()			,		[4.0, 3.0]			,		tol=1e-2   )
      accumulator.reset()
      self.assertEqual(accumulator.step			,		0   )
      _check_local_values([0.0, 0.0]			,		[0.0, 0.0]   )




